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Fundamentals of neurocontrol: a survey

Fundamentals of neurocontrol: a survey

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The articel gives a concise overview of fundamentals of neurocontrol. Both feedforward and recurrent networks are considered and the foundations of approximation of nonlinear dynamics with both structures are briefly presented. A continuous-time, state-space approach to recurrent neural networks is presented; it gives valuable insight into the dynamic behaviour of the networks and may be fast in analogue implementations. Recent learning algorithms for recurrent networks are surveyed with emphasis on the ones relevant to identification of nonlinear plants. The generalisation question is formulated for dynamic systems and discussed from the control viewpoint. A comparative study of stability is made discussing the Cohen-Grossberg and Hopfield approaches.

Inspec keywords: nonlinear dynamical systems; feedforward neural nets; recurrent neural nets; learning (artificial intelligence); neurocontrollers

Other keywords: Cohen-Grossberg approach; general theoretical framework; Hopfield approach; continuous-time state-space approach; learning algorithms; neurocontrol; dynamic behaviour; recurrent networks; nonlinear dynamics; feedforward networks

Subjects: Neurocontrol

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